


import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax

 from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=1_3     ,   __snake_case=3_0     ,   __snake_case=2     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=3_2     ,   __snake_case=5     ,   __snake_case=4     ,   __snake_case=3_7     ,   __snake_case="gelu"     ,   __snake_case=0.1     ,   __snake_case=0.1     ,   __snake_case=1_0     ,   __snake_case=0.02     ,   ):
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							is_training
         snake_case											=							use_labels
         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							type_sequence_label_size
         snake_case											=							initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         snake_case											=							(image_size // patch_size) ** 2
         snake_case											=							num_patches + 1
    def 						a_     (			self       ):
         snake_case											=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         snake_case											=							ViTConfig(
             image_size=self.image_size     ,   patch_size=self.patch_size     ,   num_channels=self.num_channels     ,   hidden_size=self.hidden_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   is_decoder=__snake_case     ,   initializer_range=self.initializer_range     ,   )

         return config, pixel_values
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							FlaxViTModel(config=__snake_case       )
         snake_case											=							model(__snake_case       )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         snake_case											=							(self.image_size, self.image_size)
         snake_case											=							(self.patch_size, self.patch_size)
         snake_case											=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, num_patches + 1, self.hidden_size)       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.type_sequence_label_size
         snake_case											=							FlaxViTForImageClassification(config=__snake_case       )
         snake_case											=							model(__snake_case       )
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         snake_case											=							1
         snake_case											=							FlaxViTForImageClassification(__snake_case       )

         snake_case											=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         snake_case											=							model(__snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.prepare_config_and_inputs()
         (
             (
             snake_case
         )					,    (
             snake_case
         )					,    
         )											=							config_and_inputs
         snake_case											=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
    def 						a_     (			self       ):
         snake_case											=							FlaxViTModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=__snake_case     ,   has_text_modality=__snake_case     ,   hidden_size=3_7       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              snake_case											=							inspect.signature(model.__call__       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              snake_case											=							[*signature.parameters.keys()]

              snake_case											=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__       ):
                   snake_case											=							self._prepare_for_class(__snake_case     ,   __snake_case       )
                   snake_case											=							model_class(__snake_case       )

                   @jax.jit
                   def model_jitted(__snake_case     ,   **__snake_case       ):
                        return model(pixel_values=__snake_case     ,   **__snake_case       )

                   with self.subTest('''JIT Enabled'''       ):
                        snake_case											=							model_jitted(**__snake_case       ).to_tuple()

                   with self.subTest('''JIT Disabled'''       ):
                        with jax.disable_jit():
                             snake_case											=							model_jitted(**__snake_case       ).to_tuple()

                   self.assertEqual(len(__snake_case       )     ,   len(__snake_case       )       )
                   for jitted_output, output in zip(__snake_case     ,   __snake_case       ):
                        self.assertEqual(jitted_output.shape     ,   output.shape       )




    @slow
    def 						a_     (			self       ):
         for model_class_name in self.all_model_classes:
              snake_case											=							model_class_name.from_pretrained('''google/vit-base-patch16-224'''       )
              snake_case											=							model(np.ones((1, 3, 2_2_4, 2_2_4)       )       )
              self.assertIsNotNone(__snake_case       )








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							tempfile.mkdtemp()

         snake_case											=							[
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''的''',
             '''价''',
             '''格''',
             '''是''',
             '''15''',
             '''便''',
             '''alex''',
             '''##andra''',
             '''，''',
             '''。''',
             '''-''',
             '''t''',
             '''shirt''',
         ]
         snake_case											=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES['''vocab_file''']       )
         with open(self.vocab_file     ,   '''w'''     ,   encoding='''utf-8'''       ) as vocab_writer:
              vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

         snake_case											=							{
             '''do_resize''': True,
             '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
             '''do_center_crop''': True,
             '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
             '''do_normalize''': True,
             '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
             '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
             '''do_convert_rgb''': True,
         }
         snake_case											=							os.path.join(self.tmpdirname     ,   __snake_case       )
         with open(self.image_processor_file     ,   '''w'''     ,   encoding='''utf-8'''       ) as fp:
              json.dump(__snake_case     ,   __snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return BertTokenizer.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return BertTokenizerFast.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							[np.random.randint(2_5_5     ,   size=(3, 3_0, 4_0_0)     ,   dtype=np.uinta       )]

         snake_case											=							[Image.fromarray(np.moveaxis(__snake_case     ,   0     ,   -1       )       ) for x in image_inputs]

         return image_inputs
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case											=							self.get_rust_tokenizer()
         snake_case											=							self.get_image_processor()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )
         processor_slow.save_pretrained(self.tmpdirname       )
         snake_case											=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,   use_fast=__snake_case       )

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )
         processor_fast.save_pretrained(self.tmpdirname       )
         snake_case											=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor_slow.tokenizer.get_vocab()     ,   tokenizer_slow.get_vocab()       )
         self.assertEqual(processor_fast.tokenizer.get_vocab()     ,   tokenizer_fast.get_vocab()       )
         self.assertEqual(tokenizer_slow.get_vocab()     ,   tokenizer_fast.get_vocab()       )
         self.assertIsInstance(processor_slow.tokenizer     ,   __snake_case       )
         self.assertIsInstance(processor_fast.tokenizer     ,   __snake_case       )

         self.assertEqual(processor_slow.image_processor.to_json_string()     ,   image_processor.to_json_string()       )
         self.assertEqual(processor_fast.image_processor.to_json_string()     ,   image_processor.to_json_string()       )
         self.assertIsInstance(processor_slow.image_processor     ,   __snake_case       )
         self.assertIsInstance(processor_fast.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         snake_case											=							self.get_tokenizer(cls_token='''(CLS)'''     ,   sep_token='''(SEP)'''       )
         snake_case											=							self.get_image_processor(do_normalize=__snake_case       )

         snake_case											=							ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname     ,   cls_token='''(CLS)'''     ,   sep_token='''(SEP)'''     ,   do_normalize=__snake_case       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.image_processor.to_json_string()     ,   image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							self.prepare_image_inputs()

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''

         snake_case											=							processor(text=__snake_case       )

         snake_case											=							tokenizer(__snake_case       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''
         snake_case											=							self.prepare_image_inputs()

         snake_case											=							processor(text=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case											=							processor.batch_decode(__snake_case       )
         snake_case											=							tokenizer.batch_decode(__snake_case       )

         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''
         snake_case											=							self.prepare_image_inputs()

         snake_case											=							processor(text=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   processor.model_input_names       )




